Last updated on May 19th, 2022 at 12:15 pm.

How to understand the upcoming trends in test automation? The incorporation of automation has revolutionized many industries and testing is no more an exception to this. The testing industry has taken a long leap in the way forward as manual methods of testing have been replaced with test automation. The incorporation of automation has replaced some of the repetitive tasks in regression testing, helping QA teams to speed up project delivery cycles. Although automation has brought significant change in the testing industry, there are still some bottlenecks – inadequate test coverage, dependency on programmers, test prioritization, maintenance, and development of automation scripts. In this article, we’ll discuss the upcoming trends in the test automation industry and how testing will going to evolve from automation to autonomous.

Upcoming trends in test automation

Incorporation of disruptive technologies – AI, ML, & NLP

Artificial Intelligence (AI): AI refers to the ability of the bots/ machines to mimic a human behavior/ mind, such as learning and problem-solving.AI also allows machines to learn for themselves from data and perform human tasks.

Machine Learning (ML): It is a subset of AI that enables machines to learn for themselves from data. ML provides machines with the capability to learn from data and use this knowledge to evolve themselves.

Natural Language Processing (NLP): It is a branch of artificial intelligence that is responsible for allowing seamless interactions between machines and humans. It is used to make the interaction between humans and machines as simple and close to natural language as possible.

Leveraging AI, ML, & NLP in Test Automation

AI, ML, & NLP can address various challenges associated with current test automation techniques. Let’s take a look at how automated testing can be enhanced further with AI and other disruptive techniques.

Test Creation& Script Design: Although enterprises have embraced agile and DevOps, software testing is still a manual-driven process. The creation of test cases is still done manually and business analysts, functional consultants, manual testers, and stakeholders participate in this to create user stories. These non-technical users create test scenario descriptions while programmers/ technical fellows create automation scripts, making it a time-consuming and costly process.

By incorporating NLP, this process can be streamlined. NLP allows business users to write test cases in natural language i.e. English that does not need to be learned. Then, AI and ML eliminate the dependency on programming professionals to create automation scripts. AL and ML-based engines will generate automated scripts by reading English test cases from spreadsheets. Thus, time spent on test creation is reduced significantly.

Test Script Maintenance: Traditional test automation frameworks use element locators to identify controls on the screen. Whenever a change happens in the object property i.e. Name, ID, Xpath, or CSS due to the introduction of new screens, buttons, user flows or a slight change in the UI, the automation scripts break. A significant amount of time and effort is needed to maintain the test scripts.

Consider a scenario of Oracle Cloud quarterly updates. In this, Oracle Cloud testing is required at least 8 times a year. Imagine how much efforts and time is required by QA teams to maintain test scripts as Oracle Cloud apps are highly dynamic in nature.

With self-healing capabilities, this problem can be addressed. Test automation frameworks that leverage AI and ML can automatically identify the change made to an element locator (ID), or a screen/flow. The self-healing engines are driven by AI and ML-based algorithms that dynamically fix the test automation scripts without human intervention.

Test Recommendation: What to test in regression testing is still a challenge. Often, test engineers are driven by their experience or guesses to pick up smoke/regression tests. This exposes your business to serious risks and some critical test cases may be overlooked.

AI can solve this problem as it can precisely recommend the minimum number of tests that need to be executed to keep business risks at bay.

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